An important factor enabling high reliability at electric power plants is to provide generators, such as wind generators, with condition monitoring systems in order to detect faults at an early stage. The invention aims to provide an improved diagnosing method for detecting and identifying magnetic faults of permanent magnet synchronous generators (PMSG), especially for wind power permanent magnet synchronous generators. Permanent magnets synchronous generators are one of the common machines in the wind generation industry. Detecting demagnetization is important since it produces degradation of the machine performance. There have been methods for detecting demagnetization based on monitoring the back EMF (electromotive force) and the stator current of the generators.
However, when monitoring those indicators, there is a risk that wrong information could be given since other faulty conditions produce similar signatures than demagnetization.
U.S. Pat. No. 7,324,008 (D1) describes analyzing an electrical machine using finite element method (FEM) analysis with at least one fault condition to be able to predict the effect of the fault condition. The result of the FEM analysis can be used to identify the analyzed faults from live measurements of the machine (see D1, abstract). D1 describes a transverse flux motor but also suggests that similar FEM analysis with a fault could be used for other electrical machines (see D1, column 7, line 26-50). D1 suggests simulating magnetic faults and the effect of the magnetic faults to the magnetic flux, so that measurements of the magnetic flux, by means of “search coils”, can be used as an indication of magnetic faults (see D1, column 1, lines 46-51). The method described in D1 provides means for detecting faults that degrade the magnetic strength of magnets due to overheating and/or demagnetization (see D1, column 6, line 15-16).
In the technical field it is also important to monitor the rotor bearings. The condition of the rotor bearings of generators for wind turbines are often monitored by measuring the vibration close to the bearings.
D1 suggests analyzing other fault conditions, such as mechanical and electrical misalignments, and suggests also using other sensors for monitoring the machine, in addition to the coils used for sensing the magnetic flux (see D1, column 7, line 27-31), such other sensors as temperature, vibration and current sensors.
The invention concerns diagnosis of permanent magnet synchronous machines, especially detecting magnetic faults of generators, and provides an alternative to using search coils.
The article “Static-, Dynamic-, and Mixed-Eccentricity Fault Diagnoses in Permanent-Magnet Synchronous Motors”, Ebrahimi et al, IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 56, NO. 11, NOVEMBER 2009 4727, (D4) indicates how static-, dynamic- and mixed-eccentricities influences the current frequency spectra with amplitudes in sideband components of the stator current frequency. Also, D4 describes how the current spectra are influenced by demagnetisation, short circuit faults and open circuit faults, and can be distinguished from the eccentricity faults by not creating sideband components. The invention provides an alternative to the current spectrum analysis for discovering demagnetisation, as described in D4, but can also be used in addition to such stator current analysis.
D1 and D4 are considered the most relevant prior art documents, but reference will in the following be made to a few documents concerning solving other technical problems than discovering demagnetization of permanent magnets in synchronous machines.
US2011/0018727 (D2) describes a method and device for performing wind turbine generator fault diagnostics, wherein sensors monitor a wind generator and signals from the sensors are analyzed to detect anomalies that indicate faults. The method that is described in D2 evaluates (see D2, FIG. 1) electrical signals (voltage, current), vibration signals and temperature signals. The electrical signals and vibration signals are subjected to respective spectral analysis (see D2, references 110 and 120) (such as an FFT or Fast Fourier Transform). The spectra are subjected to signature analysis (see D2, references 140 and 142) and anomaly detection (see D2, references 150 and 154). Transients in the temperature are detected and anomalies of the temperature identified (see D2, reference 156). Upon detecting anomalies in the electric and vibration spectra, respectively, and in the temperature, it is concluded that the generator system has electrical or mechanical faults (see D2, paragraph [0009]). Vibration signals from an accelerometer and a voltage signature determined from the generator voltage signals may be used to detect an underlying eccentricity fault (see D2, paragraph [0010]). The document describes generators in a general way and does not describe detection of magnetic faults, and especially not detecting magnetic faults in permanent magnet synchronous generators.
Analysis of the vibration signals has been used to diagnose faults of electric machines, see Jover Rodriguez, P. V., 2007, “Current-, Force-, and Vibration-Based Techniques for Induction Motor Condition Monitoring”, Doctoral Dissertation, Helsinki University of Technology, Finland (D3), which may be found at http://lib.tkk.fi/Diss/2007/isbn9789512289387/isbn9789512289387.pdf
D3 describes analyzing the frequency spectra of stator vibrations in an asynchronous motor. The aim of this research was to discover the best indicators of induction motor faults, as well as suitable techniques for monitoring the condition of induction motors. D3 describes the effects of electromagnetic force on the vibration pattern when the motor is working under fault conditions. Moreover, D3 describes a method that allows the prediction of the effect of the electromechanical faults in the force distribution and vibration pattern of the induction machines. In D3, FEM computations are utilized, which show the force distributions acting on the stator of the electrical machine when it is working under an electrical fault. It is shown that these force components are able to produce forced vibration in the stator of the machine. The results were supported by vibration measurements. The low-frequency components could constitute the primary indicator in a condition monitoring system. D3 uses vibrational analysis to detect faults of an induction motor, which faults are broken rotor bars, broken end ring, inter-turn short circuit, bearing and eccentricity failures.